Description
This monograph develops a unified framework for understanding human and artificial behavior through the joint lenses of entropy, interference, and geometric field theory. It begins from an empirical observation: classical probabilistic and functional models fail systematically in the presence of context dependence, order effects, and behavioral instability. To address this, the work introduces entropy signatures - multi-dimensional vector - as ameasurement system capturing uncertainty, non-additivity, temporal volatility, and relationaldiversity across behavioral systems. The empirical foundation of this approach builds in part on the work of Roger Yau, “EntropySignatures of Behavioral Transitions: A Quantum-Like Framework for Human and AI Decision Analysis”, which demonstrates that multi-dimensional entropy measures can effectively detect and predict behavioral phase changes across human and AI domains. The present monograph extendsthis line of research from a measurement framework to a full field-theoretic ontology.